SpotWhisk: Transferring the Spot Instances Cost-Efficiency to Serverless Data Analytics Pipelines

Abstract

Function-as-a-Service (FaaS) has revolutionized cloud computing by offering an unprecedented model, with key advantages such as: (1) no infrastructure management, (2) high scalability with fast startup times, and (3) pay-as-you-go pricing model. However, despite its benefits, the high costs of FaaS compared to other computational alternatives lead to increased execution costs for some workloads. This paper presents SpotWhisk, an open-source FaaS-like platform that replaces the expensive computational units of FaaS with spot instances: preemptible virtual machines offered by the provider, which are up to 87.4% more economical than traditional FaaS computational units. SpotWhisk, which maintains the same programming model and infrastructure abstraction as FaaS, allows users to easily migrate their serverless data analytics pipelines and schedule new functions using the same simplified model. Although SpotWhisk introduces the overhead of initializing the underlying spot instances, we demonstrate, through real-world workloads, that: (1) we achieve a very low eviction rate for FaaS workloads, (2) we achieve savings of up to 84% in execution costs, and (3) we improve the overall performance (time-to-cost ratio) of the evaluated applications by up to 6.15×.